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Optimal approximation of linear systems by artificial immune response

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Abstract

This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.

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Authors and Affiliations

  1. Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, China

    Gong Maoguo, Du Haifeng & Jiao Licheng

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Du Haifeng

Authors
  1. Gong Maoguo
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  2. Du Haifeng
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  3. Jiao Licheng
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Correspondence to Gong Maoguo.

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Gong, M., Du, H. & Jiao, L. Optimal approximation of linear systems by artificial immune response. SCI CHINA SER F 49, 63–79 (2006). https://doi.org/10.1007/s11432-005-0314-x

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  • DOI: https://doi.org/10.1007/s11432-005-0314-x

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